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DTSTART:20210314T100000
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DTSTART;TZID=America/Los_Angeles:20210423T133000
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SUMMARY:ML-Opt@UW: Yue Sun
DESCRIPTION:Title: Subspace Based Meta-learning\nAbstract:\nMeta-learning typically involves two phases. First\, one learns a suitable representation from the previously seen tasks. Secondly\, this representation is used for learning a new task using only a few samples (i.e.\, few-shot learning). In this talk I will discuss:\n1. Linear meta learning: sample complexity of representation learning with general covariance\n2. Linear meta learning: algorithm & analysis for overparameterized few-shot learning\n3. Generalization to nonlinear meta-learning\n\n\nBio:\nYue Sun is a 5th year PhD student from University of Washington\, Seattle. He is interested in theoretical understanding of optimization\, ML and control. His research works are about:\n1. Nonconvex optimization on Riemannian manifolds (UW)\n2. Low order linear system identification (UW)\n3. Subspace based meta-learning (UW)\n4. Nonconvex optimization applied in optimal control (UW)\n\n5. Online optimization for video coding (Google\, 2019)\n6. Compressive sensing and phase retrieval (Ohio State U\, 2015; Nokia Bell Labs\, 2021)
URL:https://ifds.info/event/mloptuw/
CATEGORIES:MLOpt@UWash
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